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184 Results

Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems

Staff Working Paper 2024-15 Ajit Desai, Anneke Kosse, Jacob Sharples
Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems.

Forecasting Recessions in Canada: An Autoregressive Probit Model Approach

Staff Working Paper 2024-10 Antoine Poulin-Moore, Kerem Tuzcuoglu
We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature.

Predictive Density Combination Using a Tree-Based Synthesis Function

This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53

Making It Real: Bringing Research Models into Central Bank Projections

Staff Discussion Paper 2023-29 Marc-André Gosselin, Sharon Kozicki
Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment.
Content Type(s): Staff research, Staff discussion papers Topic(s): Economic models, Monetary policy JEL Code(s): C, C3, C32, C5, C51, E, E3, E37, E4, E47, E5, E52

Testing Collusion and Cooperation in Binary Choice Games

Staff Working Paper 2023-58 Erhao Xie
This paper studies the testable implication of players’ collusive or cooperative behaviour in a binary choice game with complete information. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart.

Machine learning for economics research: when, what and how

Staff Analytical Note 2023-16 Ajit Desai
This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.

Identifying Nascent High-Growth Firms Using Machine Learning

Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald
Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models

Staff Discussion Paper 2023-23 Donald Coletti
The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

Predicting Changes in Canadian Housing Markets with Machine Learning

Staff Discussion Paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.
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